The 2012 International Joint Conference on Neural Networks (IJCNN)最新文献_第8页

A group-decision making model of orientation detection 方向检测的群体决策模型

The 2012 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2012-06-10 DOI: 10.1109/IJCNN.2012.6252662

Hui Wei, Yuan Ren, Zheyan Wang

{"title":"A group-decision making model of orientation detection","authors":"Hui Wei, Yuan Ren, Zheyan Wang","doi":"10.1109/IJCNN.2012.6252662","DOIUrl":"https://doi.org/10.1109/IJCNN.2012.6252662","url":null,"abstract":"The feedforward model proposed by Hubel and Wiesel partially explained orientation selectivity in simple cells. This classical hypothesis attributed orientation preference to idealized alignment of geniculate cell receptive fields. Many scholars have been either revising this model or putting forward new theories to account for more related phenomenon such as contrast invariant tuning. None of the previous neural models is complete in implementation details or involves strict computational strategies. This paper mathematically studied a detailed but vital question which has long been neglected: the possibility of massive variable-sized, unaligned geniculate cell receptive fields producing the orientation selectivity of a simple cell. The response curve of each afferent neuron is fully utilized to obtain a local constraint and a group-decision making approach is then applied to solve the constraint satisfaction problem. Our new model does not achieve just consistent experimental results with physiological data, but consistent interpretations of several illusions with observers' perceptions. The current work, which supplemented the previous models with necessary computational details, is based on ensemble coding in essence. This underlying mechanism helps to understand how visual information is processed in from the retina to the cortex.","PeriodicalId":287844,"journal":{"name":"The 2012 International Joint Conference on Neural Networks (IJCNN)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127398381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

Online prediction of time series data with recurrent kernels 基于循环核的时间序列数据在线预测

The 2012 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2012-06-10 DOI: 10.1109/IJCNN.2012.6252747

Zhao Xu, Q. Song, Haijin Fan, Danwei W. Wang

引用次数: 5

Visualising large-scale neural network models in real-time 实时可视化大规模神经网络模型

The 2012 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2012-06-10 DOI: 10.1109/IJCNN.2012.6252490

Cameron Patterson, F. Galluppi, Alexander D. Rast, S. Furber

{"title":"Visualising large-scale neural network models in real-time","authors":"Cameron Patterson, F. Galluppi, Alexander D. Rast, S. Furber","doi":"10.1109/IJCNN.2012.6252490","DOIUrl":"https://doi.org/10.1109/IJCNN.2012.6252490","url":null,"abstract":"As models of neural networks scale in concert with increasing computational performance, gaining insight into their operation becomes increasingly important. This paper proposes an efficient and generalised method to access simulation data via in-system aggregation, providing visualised representation at all layers of the network in real-time. Enabling neural networks for real-time visualisation allows a user to gain insight into the network dynamics of their systems as they operate over time. This visibility also permits users (or a computational agent) to determine whether early intervention is required to adjust parameters, or even to terminate operation of experimental networks that are not operating correctly. Conventionally the determination of correctness would occur post-simulation, so with sufficient `in-flight' insight, a significant advantage may be obtained, and compute time minimised. For this paper we apply the real-time visualisation platform to the SpiNNaker programmable neuromimetic system and a variety of neural network models. The visualisation platform is shown to be capable across a range of diverse simulations, and at supporting differing layers of network abstraction, requiring minimal configuration to represent each model. The resulting general-purpose visualisation platform for neural networks, is effective at presenting data to users in order to aid their comprehension of the network dynamics during operation, and scales from small to biologically-significant network sizes.","PeriodicalId":287844,"journal":{"name":"The 2012 International Joint Conference on Neural Networks (IJCNN)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129984282","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Decision making guided by emotion 由情感引导的决策

The 2012 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2012-06-10 DOI: 10.1109/IJCNN.2012.6252412

D. Béroule, P. Gisquet-Verrier

引用次数: 1

Optimizing the moving average 优化移动平均线

The 2012 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2012-06-10 DOI: 10.1109/IJCNN.2012.6252797

Adrian Letchford, Junbin Gao, Lihong Zheng

引用次数: 4

Performance of quadratic assignment problem by hopfield NN with periodic brake 带周期制动的hopfield神经网络二次分配问题的性能

The 2012 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2012-06-10 DOI: 10.1109/IJCNN.2012.6252551

Hironori Kumeno, Y. Uwate, Y. Nishio

引用次数: 1

A sequence based dynamic SOM model for text clustering 基于序列的动态SOM文本聚类模型

The 2012 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2012-06-10 DOI: 10.1109/IJCNN.2012.6252474

Upuli Gunasinghe, S. Matharage, D. Alahakoon

引用次数: 6

Towards improved theoretical problems for autonomous discovery 改进自主发现的理论问题

The 2012 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2012-06-10 DOI: 10.1109/IJCNN.2012.6252456

C. Lovell, S. Gunn

{"title":"Towards improved theoretical problems for autonomous discovery","authors":"C. Lovell, S. Gunn","doi":"10.1109/IJCNN.2012.6252456","DOIUrl":"https://doi.org/10.1109/IJCNN.2012.6252456","url":null,"abstract":"Active learning and experimental data acquisition address the same problems, understanding a system under investigation with as few resources as possible. However there are few instances where the theoretically principled techniques in active learning or sequential experimental design have been applied to managing data acquisition in physical experimentation. Partly this is due to fundamental differences between the problems investigated within active learning and the issues faced in much physical experimentation. From a previous study we conducted into autonomous experimentation, where we developed a system capable of automatically designing experiments and proposing potential hypotheses, we aim to investigate and highlight the differences between theoretical active learning and the requirements of experimentalists. We also propose an update of the multi-armed bandit problem that provides a theoretical problem more closely aligned to that found in physical experimentation. We believe that for active learning techniques to be used more widely as tools within physical experimentation, a greater focus of research has to be placed on theoretical problems that have assumptions more closely aligned to those found commonly within physical experimentation. Assumptions such as extremely limited resources, more so than typically considered in active learning problems, along with erroneous observations or noisy oracles, should become standard features of active learning problems, as in experimentation there are rarely enough resources available to be certain about the validity of the data obtained and the quality of the hypotheses produced.","PeriodicalId":287844,"journal":{"name":"The 2012 International Joint Conference on Neural Networks (IJCNN)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132143538","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dynamic initiation and dual-tree complex wavelet feature-based classification of motor imagery of swallow EEG signals 基于动态起始和双树复小波特征的吞咽脑电信号运动图像分类

The 2012 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2012-06-10 DOI: 10.1109/IJCNN.2012.6252603

Huijuan Yang, Cuntai Guan, K. Ang, C. Wang, K. Phua, Juanhong Yu

{"title":"Dynamic initiation and dual-tree complex wavelet feature-based classification of motor imagery of swallow EEG signals","authors":"Huijuan Yang, Cuntai Guan, K. Ang, C. Wang, K. Phua, Juanhong Yu","doi":"10.1109/IJCNN.2012.6252603","DOIUrl":"https://doi.org/10.1109/IJCNN.2012.6252603","url":null,"abstract":"The use of motor imagery-based brain computer interface has recently been shown to have potential for rehabilitation. This paper proposes a novel scheme to detect motor imagery of swallow from electroencephalography (EEG) signals for dysphagia rehabilitation. The proposed scheme extracts features from the coefficients of dual-tree complex wavelet transform (DT-CWT). A novel sliding window-based peak localization scheme is proposed to dynamically locate the initiation of tongue movement from Electromyography (EMG) signal. Subsequently, effective time segments are extracted from EEG signal for classification based on the detected dynamic initiation location. Comparisons are made between our proposed scheme with that of the three existing approaches. The results based on six healthy subjects show that an increase in averaged accuracy of 9.95% is achieved. Further, an increase in averaged accuracy of 8.02% is resulted comparing our proposed scheme by using and not using the dynamic initiation to extract the time segments. Classification results using EMG data confirm that our results are not due to movements artifacts. Statistical tests with 95% confidence to estimate the accuracy on the respective action at chance level show that five out of six subjects performed above chance level for our proposed dynamic initiation and wavelet feature-based approach.","PeriodicalId":287844,"journal":{"name":"The 2012 International Joint Conference on Neural Networks (IJCNN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130969443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 25

Influence of unstable patterns in layered cluster oriented ensemble classifier 面向分层簇的集成分类器中不稳定模式的影响

The 2012 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2012-06-10 DOI: 10.1109/IJCNN.2012.6252421

Ashfaqur Rahman, B. Verma

{"title":"Influence of unstable patterns in layered cluster oriented ensemble classifier","authors":"Ashfaqur Rahman, B. Verma","doi":"10.1109/IJCNN.2012.6252421","DOIUrl":"https://doi.org/10.1109/IJCNN.2012.6252421","url":null,"abstract":"In this paper, we have investigated the influence of cluster instability on the performance of layered cluster oriented ensemble classifier. The final contents of clusters in some clustering algorithms like k-means depend on the initialization of clustering parameters like cluster centres. Layered cluster oriented ensemble classifier is based on this philosophy where the base classifiers are trained on clusters generated at multiple layers from random initialization of cluster centres. As the data is clustered into multiple layers some patterns move between clusters (unstable patterns). This instability of patterns brings in diversity among the base classifiers that in turn influences the accuracy of the ensemble classifier. There is thus a connection between the instability of the patterns and the accuracy of layered cluster oriented ensemble classifier. The research presented in this paper aims to find this connection by investigating the influence of unstable patterns on the overall ensemble classifier accuracy as well as diversity among the base classifiers. We have provided results from a number of experiments to quantify this influence.","PeriodicalId":287844,"journal":{"name":"The 2012 International Joint Conference on Neural Networks (IJCNN)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130442588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5